Databricks excels at unified data analytics with Apache Spark-based processing and AI/ML capabilities, while Snowflake specializes in cloud data warehousing with superior query performance and broader SQL compatibility. Databricks is better for complex data processing pipelines, while Snowflake is better for traditional analytics and business intelligence.
Databricks vs Snowflake — Side-by-side comparison on A Versus B
Choose Databricks if you need unified data processing with strong ML/AI capabilities, require ETL/ELT pipeline flexibility, or want to avoid vendor lock-in with open data formats like Delta Lake. Choose Snowflake if you prioritize fast SQL queries for analytics, prefer a simpler data warehouse architecture, have primarily structured data, or need the broadest ecosystem of third-party BI tool integrations.
Unified lakehouse platform combining data warehousing with Apache Spark-based processing and native ML capabilities.
Cloud-agnostic data platform with decoupled compute/storage and instant elasticity.
Users Reporting High Satisfaction (2025 G2)
Databricks vs Snowflake — Comparison Data
Structured comparison data for Databricks vs Snowflake with 157 attributes.
SQL Query Performance (sample 1TB table)
BigQuery/Equivalent Query Speed (1TB dataset)
Data Governance (Unity Catalog equivalent)
Minimum Learning Curve (months for competency)
Starting Monthly Cost (1 TB storage + compute)
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
Required Team Skills (FTE equivalents for operations)
Query Latency (median, standard ETL workload)
Built-in Collaboration Tools (notebooks, dashboards, repos)
Starting Monthly Cost (10GB active data)
SQL Query Performance (TPC-DS Benchmark)
Maximum Concurrent Queries Per Warehouse
Annual TCO (100TB storage, average usage)
Databricks vs Snowflake — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Databricks vs Snowflake.
Which is cheaper for small data teams under 10GB?
Snowflake is typically 25-30% cheaper at small scale ($480-600/month vs Databricks $650-800/month). However, Databricks' open data format saves expensive data egress fees if you later migrate, offsetting some cost difference over 2-3 years.
Can I run machine learning workloads on both platforms?
Yes, but differently. Databricks has native MLflow integration and Spark ML built-in, making ML workflows seamless. Snowflake requires external tools (Python/scikit-learn in notebooks) or its weaker Cortex AI, adding 3-5 integration steps.
What happens if I want to switch from Snowflake to another platform?
Switching is difficult. Snowflake uses a proprietary format; exporting large datasets incurs 10-15% egress overhead and requires data reprocessing. Databricks' Delta Lake format is open-source and portable to other Apache Spark or Iceberg-compatible platforms, reducing switching costs by ~60%.
Which handles real-time streaming data better?
Databricks excels at real-time streaming via Apache Spark Structured Streaming with sub-second latency. Snowflake handles streaming through Snowpipe (file-based, 1-5 minute latency) or external Kafka connectors, making it less suitable for true real-time requirements.
How do query speeds compare for standard business analytics?
Snowflake is 35-40% faster on structured SQL analytics (TPC-DS benchmark: 28s vs 45s). This matters for BI dashboards refreshing 50+ times daily. Databricks closes this gap for unstructured data and complex multi-stage transformations where Spark's parallel processing wins.